CaPTk is a software platform, written in C++, for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk integrates advanced, validated tools performing various aspects of medical image analysis that have been developed in the context of active clinical research studies and collaborations toward addressing real clinical needs. With emphasis given on being a very lightweight and efficient image viewer and eliminating the prerequisite for a substantial computational background, CaPTk aims to facilitate the swift translation of advanced computational algorithms into routine clinical quantification, analysis, decision making, and reporting workflow.

Its long-term goal is to provide widely used technology that makes use of advanced imaging analytics in cancer prediction, diagnosis and prognosis, as well as in better understanding the biological mechanisms of cancer development.

The package leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. At the first level, image analysis algorithms are used to extract a rich panel of diverse and complementary features, such as multi-parametric intensity histograms, textural, morphologic, and kinetic variables, connectomics, and spatial patterns. At the second level, these radiomic features are fed into multi-variable machine learning models to produce diagnostic, prognostic and predictive biomarkers (specific examples are given in the “Scientific Findings” section). Fig. 1 shows results from clinical studies in three areas:

CaPTk is developed and maintained by the Center for Biomedical Image Computing and Analytics (CBICA - https://www.cbica.upenn.edu) at the University of Pennsylvania, and draws upon research from several groups within the Center and beyond.

Fig.1. Overview of all functions and applications of CaPTk, in its two-level architecture